An Application of Set Based Concurrent Engineering Method using Machine Learning to a Vehicle Development
機械学習を活用したセットベース最適化手法の車両開発への応用
- Delivery
- Available on this site
- Format
- Price
- Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880
- Publication code
- 20216125
- Paper/Info type
- Proceedings (Autumn)
No.108-21
- Pages
- 1-5(Total 5 p)
- Date of publication
- Oct 2021
- Publisher
- JSAE
- Language
- Japanese
- Event
- 2021 JSAE Annual Congress (Autumn) [Online]
Detailed Information
| Author(J) | 1) 小西 祐介, 2) 新谷 浩平 |
|---|---|
| Author(E) | 1) Yusuke Konishi, 2) Kohei Shintani |
| Affiliation(J) | 1) トヨタ自動車, 2) トヨタ自動車 |
| Affiliation(E) | 1) Toyota Motor, 2) Toyota Motor |
| Abstract(J) | SDGsの達成には，ゼロ・エミッション達成車両の開発を加速させる必要がある．そのため効率的な開発が求められる中，大規模で複雑な設計問題を分解して最適化するセットベース最適化手法が提唱されている．本論文では，機械学習を活用したセットベース最適化手法を車両開発に応用し，複数性能の成立範囲を探索した結果を示す． Translation |
| Abstract(E) | Acceleration of zero emission vehicle development are required to achieve SDGs and Carbon neutral. For this reason, efficient vehicle development is important. A set based concurrent engineering method has been proposed as an efficient target setting method to decompose a large and complex design problems down to sub-problems. Based on this method, it is important to clarify the feasible region. In this paper, we show an application of this method to a vehicle development. In the application, regression models are constructed and used in optimization problems to find feasible region. |
